Density‐based clustering
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Hans-Peter Kriegel | Peer Kröger | Arthur Zimek | Jörg Sander | A. Zimek | H. Kriegel | J. Sander | P. Kröger | Peer Kröger | Arthur Zimek
[1] Ira Assent,et al. Clustering high dimensional data , 2012 .
[2] Hans-Peter Kriegel,et al. Data bubbles: quality preserving performance boosting for hierarchical clustering , 2001, SIGMOD '01.
[3] Jörg Sander,et al. Semi-supervised Density-Based Clustering , 2009, 2009 Ninth IEEE International Conference on Data Mining.
[4] Daniel Barbará,et al. Requirements for clustering data streams , 2002, SKDD.
[5] Hans-Peter Kriegel,et al. Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications , 1998, Data Mining and Knowledge Discovery.
[6] Michalis Vazirgiannis,et al. A density-based cluster validity approach using multi-representatives , 2008, Pattern Recognit. Lett..
[7] Elke Achtert,et al. Robust, Complete, and Efficient Correlation Clustering , 2007, SDM.
[8] Fionn Murtagh,et al. A Survey of Algorithms for Contiguity-Constrained Clustering and Related Problems , 1985, Comput. J..
[9] L. Devroye,et al. The Strong Uniform Consistency of Nearest Neighbor Density Estimates. , 1977 .
[10] J. Hartigan. Statistical theory in clustering , 1985 .
[11] J. MacQueen. Some methods for classification and analysis of multivariate observations , 1967 .
[12] J. Yackel,et al. Consistency Properties of Nearest Neighbor Density Function Estimators , 1977 .
[13] Robin Sibson,et al. SLINK: An Optimally Efficient Algorithm for the Single-Link Cluster Method , 1973, Comput. J..
[14] Ira Assent,et al. EDSC: efficient density-based subspace clustering , 2008, CIKM '08.
[15] Vipin Kumar,et al. Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data , 2003, SDM.
[16] J. Carmichael,et al. FINDING NATURAL CLUSTERS , 1968 .
[17] Daniel A. Keim,et al. An Efficient Approach to Clustering in Large Multimedia Databases with Noise , 1998, KDD.
[18] Ian H. Witten,et al. The WEKA data mining software: an update , 2009, SKDD.
[19] C. Quesenberry,et al. A nonparametric estimate of a multivariate density function , 1965 .
[20] Hans-Peter Kriegel,et al. Density-Connected Subspace Clustering for High-Dimensional Data , 2004, SDM.
[21] Tommy W. S. Chow,et al. Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density , 2004, Pattern Recognit..
[22] J. Hartigan. Direct Clustering of a Data Matrix , 1972 .
[23] David M. Raup,et al. Geometric analysis of shell coiling; general problems , 1966 .
[24] Robin Sibson,et al. The Construction of Hierarchic and Non-Hierarchic Classifications , 1968, Comput. J..
[25] Hans-Peter Kriegel,et al. OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.
[26] Hans-Peter Kriegel,et al. A survey on unsupervised outlier detection in high‐dimensional numerical data , 2012, Stat. Anal. Data Min..
[27] Elke Achtert,et al. On Exploring Complex Relationships of Correlation Clusters , 2007, 19th International Conference on Scientific and Statistical Database Management (SSDBM 2007).
[28] Tian Zhang,et al. BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.
[29] E. Parzen. On Estimation of a Probability Density Function and Mode , 1962 .
[30] Thomas Seidl,et al. Subspace correlation clustering: finding locally correlated dimensions in subspace projections of the data , 2012, KDD.
[31] Ira Assent,et al. DUSC: Dimensionality Unbiased Subspace Clustering , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).
[32] Zhiyong Lu,et al. Automatic Extraction of Clusters from Hierarchical Clustering Representations , 2003, PAKDD.
[33] G. N. Lance,et al. A General Theory of Classificatory Sorting Strategies: 1. Hierarchical Systems , 1967, Comput. J..
[34] Christian Böhm,et al. Computing Clusters of Correlation Connected objects , 2004, SIGMOD '04.
[35] Christian Böhm,et al. Density connected clustering with local subspace preferences , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).
[36] W. T. Williams,et al. Multivariate Methods in Plant Ecology: V. Similarity Analyses and Information-Analysis , 1966 .
[37] Christian Böhm,et al. HISSCLU: a hierarchical density-based method for semi-supervised clustering , 2008, EDBT '08.
[38] R. Fisher. THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .
[39] M. Rosenblatt. Remarks on Some Nonparametric Estimates of a Density Function , 1956 .
[40] A. Cuevas,et al. Cluster analysis: a further approach based on density estimation , 2001 .
[41] Werner Stuetzle,et al. Estimating the Cluster Tree of a Density by Analyzing the Minimal Spanning Tree of a Sample , 2003, J. Classif..
[42] P. Sneath. The application of computers to taxonomy. , 1957, Journal of general microbiology.
[43] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[44] Hans-Peter Kriegel,et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.
[45] Hans-Peter Kriegel,et al. Density-based Projected Clustering over High Dimensional Data Streams , 2012, SDM.
[46] Vincent Kanade,et al. Clustering Algorithms , 2021, Wireless RF Energy Transfer in the Massive IoT Era.
[47] Elke Achtert,et al. Visual Evaluation of Outlier Detection Models , 2010, DASFAA.
[48] Hans-Peter Kriegel,et al. Subspace clustering , 2012, WIREs Data Mining Knowl. Discov..
[49] Ricardo J. G. B. Campello,et al. Relative clustering validity criteria: A comparative overview , 2010, Stat. Anal. Data Min..
[50] Ira Assent,et al. Relevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional Data , 2009, 2009 Ninth IEEE International Conference on Data Mining.
[51] Hans-Peter Kriegel,et al. Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering , 2009, TKDD.
[52] W. Fitch,et al. Construction of phylogenetic trees. , 1967, Science.
[53] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[54] Chenghu Zhou,et al. DECODE: a new method for discovering clusters of different densities in spatial data , 2009, Data Mining and Knowledge Discovery.
[55] Hans-Peter Kriegel,et al. Can Shared-Neighbor Distances Defeat the Curse of Dimensionality? , 2010, SSDBM.